Question

在DataFrame中，我想计算每一列的值并将其用作索引。

我想打开它：

Q1                   Q2                  Q3
Strongly agree       Agree               Undecided
Undecided            Agree               More or less disagree
Strongly agree       Agree               Undecided
Strongly agree       Strongly Disagree   Disagree
More or less agree   Undecided           Strongly disagree

为此：

                        Q1  Q2  Q3
Strongly agree          3   0   0
Agree                   0   3   0
More or less agree      1   0   0
Undecided               1   1   2
More or less disagree   0   0   1
Disagree                0   0   1
Strongly disagree       0   1   1

大熊猫怎么可能？

Answer 1

如果您坚持使用value_counts，则可以先使用stack和groupby，然后在value_counts之前致电unstacking：

df.stack().groupby(level=[1]).value_counts().unstack(0, fill_value=0)

                       Q1  Q2  Q3
Agree                   0   3   0
Disagree                0   0   1
More or less agree      1   0   0
More or less disagree   0   0   1
Strongly Disagree       0   1   0
Strongly agree          3   0   0
Strongly disagree       0   0   1
Undecided               1   1   2

另一种选择是使用melt和pivot_table：

(df.melt()
   .pivot_table(columns='variable', index='value', aggfunc='size', fill_value=0))

variable               Q1  Q2  Q3
value                            
Agree                   0   3   0
Disagree                0   0   1
More or less agree      1   0   0
More or less disagree   0   0   1
Strongly Disagree       0   1   0
Strongly agree          3   0   0
Strongly disagree       0   0   1
Undecided               1   1   2

使用crosstab的解决方案：

v = df.melt()
pd.crosstab(v['value'], v['variable'])

variable               Q1  Q2  Q3
value                            
Agree                   0   3   0
Disagree                0   0   1
More or less agree      1   0   0
More or less disagree   0   0   1
Strongly Disagree       0   1   0
Strongly agree          3   0   0
Strongly disagree       0   0   1
Undecided               1   1   2

Answer 2

您可以将const函数应用于整个数据框，并用0填充let值。

let

如何在DataFrame上应用Series.value_counts（）？

2 个答案: